Radar Tracker: Moving Instance Tracking in Sparse and Noisy Radar Point Clouds

Abstract

Robots and autonomous vehicles should be aware of what happens in their surroundings. The segmentation and tracking of moving objects are essential for reliable path planning, including collision avoidance. We investigate this estimation task for vehicles using radar sensing. We address moving instance tracking in sparse radar point clouds to enhance scene interpretation. We propose a learning-based radar tracker incorporating temporal offset predictions to enable direct center-based association and enhance segmentation performance by including additional motion cues. We implement attention-based tracking for sparse radar scans to include appearance features and enhance performance. The final association combines geometric and appearance features to overcome the limitations of center-based tracking to associate instances reliably. Our approach shows an improved performance on the moving instance tracking benchmark of the RadarScenes dataset compared to the current state of the art.

arXiv commentProc. of the IEEE Intl. Conf. on Robotics & Automation (ICRA)

References in corpus (5)

• PCT: Point cloud transformer
• Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data
• Gaussian Radar Transformer for Semantic Segmentation in Noisy Radar Data
• GFNet: Geometric Flow Network for 3D Point Cloud Semantic Segmentation
• Radar Velocity Transformer: Single-scan Moving Object Segmentation in Noisy Radar Point Clouds

Cited by in corpus (1)

• SemRaFiner: Panoptic Segmentation in Sparse and Noisy Radar Point Clouds